Current autonomous driving path planning interface between an autonomous driver controller and a powertrain controller is a simple powertrain torque (or vehicle longitudinal acceleration/deceleration request) at a current point in time. This is a similar type of interface to the powertrain controller as compared to a cruise control system and may lead to incorrect vehicle path planning if the powertrain is unable to deliver the required torque (or vehicle acceleration/deceleration) such that the vehicle is able to travel the desired autonomous driving path. This is especially important in a vehicle lane change or passing maneuver, where powertrain capability could suddenly change, or saturate, in terms of vehicle acceleration, potentially leading to an unsafe situation as shown in FIG. 1 at time, t1. This situation may occur due to multiple conditions within the powertrain including traction drive operating limits, high voltage power limits, thermal management, etc. Typical autonomous path planning algorithms output a target vehicle acceleration (or wheel torque) request during the autonomous driving maneuver at the current vehicle operating state without future (or predictive) knowledge if the powertrain components would be able to meet the target profile (shown in FIG. 1). If the powertrain components are unable to meet the target vehicle acceleration demand during the autonomous driving maneuver, one existing solution has been to update to a new target path plan as the autonomous driving maneuver is being performed, or deactivate the autonomous driving function, notify the driver, and return control of the vehicle back to the driver during the maneuver. This is not optimal and potentially unsafe when performing an autonomous driving maneuver. One potential negative outcome would be loss of vehicle acceleration during the passing portion of the autonomous driving maneuver (during time t1) while another vehicle is approaching the vehicle performing the autonomous driving maneuver.
Accordingly, there exists a need for a strategy for predicting the capability of various powertrain components, and alter the autonomous driving path of the vehicle based on a change in the capability of the powertrain components, where there is an optimized powertrain control strategy for acceleration/deceleration control of a fully autonomous or semi-autonomous driving vehicle.